Evidence map›Paper›PMID 41883620›Full record

ArticleClinical interventions in aging2026

Association Rule Analysis of Cognitive Frailty Subtypes in Community-Dwelling Older Adults.

Shicong Liang, Xiaoxing Lai, Hongshuang Chen, Qingchi Wang, Xiaopeng Huo

Abstract read
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Article in Clinical interventions in aging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Shicong LiangSchool of Nursing, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China.
Xiaoxing LaiDepartment of Neurology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China.
Hongshuang ChenNursing Department, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China.
Qingchi WangNursing Department, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China.
Xiaopeng HuoNursing Department, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: In order to provide a foundation for identification and intervention strategies, this study intends to investigate the influencing factors and associative patterns of two subtypes of reversible cognitive frailty (RCF) and potential reversible cognitive frailty (PRCF) in older adults with cognitive frailty (CF) who live in the community. Patients and Methods: From July to December 2023, we conducted a cross-sectional study in a Beijing community to recruit older adults with CF using convenience sampling. We conducted the survey using the General Information Questionnaire, the Montreal Cognitive Assessment, the Clinical Dementia Rating, the Fried Frailty Phenotype, the Geriatric Depression Scale-15, the Generalized Anxiety Disorder-7, the Athens Insomnia Scale, the Barthel index, the Tinetti Performance Oriented Mobility Assessment, and the Lubben social network scale. The participants were separated into two categories: RCF and PRCF, based on frailty and cognitive assessment. After screening variables with a random forest algorithm, we applied association rule analysis to examine the factors influencing CF and the strength of their interactions. Results: The survey was completed by 529 older adults with CF who lived in the community. Among them, 145 participants (27.4%) were classified as PRCF and 384 (72.6%) as RCF. 24 association rules, 12 for each subtype, were developed using the Apriori algorithm and clinical practice experience. These rules identified polypharmacy, multimorbidity, low educational attainment, and high fall risk as significant factors. Furthermore, the association pattern for PRCF is more complex. Conclusion: The influencing factors associative patterns of the two categories of CF differ. In order to better manage elderly individuals with CF in the community, improve the cognitive health of the elderly, and encourage healthy aging, medical professionals should upgrade the community evaluation system for CF.

Indexed as

Cognitive DysfunctionFrail ElderlyFrailtyGeriatric AssessmentIndependent LivingAgedAged, 80 and overBeijingCognitionCross-Sectional StudiesFemaleHumansMaleRandom ForestSurveys and Questionnairesapriori algorithmassociation rule analysiscognitive frailtyolder adults

Identifiers

PMID41883620
PMCPMC13012546

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.